2-D Digital Predistortion (2-D-DPD) Architecture for Concurrent Dual-Band Transmitters
Bibliographic record
Abstract
This paper presents a novel 2-D digital-predistortion (2-D-DPD) technique that is applicable for linearization of concurrent dual-band transmitters. This technique uses a unique way for distortion compensation and linearization of dual-band transmitters by selecting, characterizing, and applying predistortion in each band separately. Compared to conventional linearization techniques, this 2-D-DPD method requires a lower sampling rate for digital-to-analog and analog-to-digital converters. The performance of the 2-D-DPD topology is evaluated using two modulated signals, Worldwide Interoperability for Microwave Access and wideband code-division multiple-access, separated in frequency by 100 MHz. The measurement results show an adjacent channel power ratio of less than -50 dBc and a normalized mean square error of less than -40 dB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".